A semantic network is a directed graph consisting of vertices, which represent concepts, and edges which represent semantic relationships between concepts. Semantic networking is a process of developing these graphs. A key part of developing semantic graphs is the provision of concept definitions and concept relationships. The present invention addresses this issue.
A semantic network can, in essence, be viewed as a knowledge representation. A knowledge representation is a way to model and store knowledge so that a computer-implemented program may process and use it. In the present context, specifically, knowledge representation may be viewed as a rule-based modeling of natural language from a computational perspective. The substantive value of a knowledge representation is accumulative in nature and as such increases with the amount of knowledge that can be captured and encoded by a computerized facility within a particular model.
One problem associated with an unbounded knowledge representation, is that current systems may impose significant barriers to scale. This is one reason why knowledge representations are often very difficult to prepare. Further, their technical complexity and precision may impose intellectual and time constraints that limit their generation and use. Further, existing systems are generally directed to the analysis and retrieval of knowledge representation from existing forms such as documents and unstructured text. With these analysis and retrieval systems, the amount of knowledge extracted is necessarily limited to the amount of knowledge that was captured in the existing forms. They may not include all the potential for new knowledge that may be derivable from these documents.
As an example of these problems, consider the following application, typical of the current approach: A product support knowledge base comprising a collection of documents is made available to customers to address their questions about one or more products. The documents are annotated by the publisher with semantic data to describe in minute, machine-readable detail the subject matter of the documents. These documents are then made available through a search tool to provide the customers with the documents most relevant to their queries.
The problem with this application is that the breadth of knowledge encapsulated by the system is bounded by the documents contained within the knowledge base (as expressed through the explicit semantic representations of concept definitions and relationships). People, however, are able to create new knowledge that is inspired by the documents that they read. Continuing the example above, as customers read documents that are related to their needs, they are able to extrapolate from this existing knowledge into the very precise solutions they seek to their problems, creating new knowledge in the process. Unfortunately, there does not yet exist a technical solution that mirrors in a computer-implemented system this process of conceptual extrapolation. The publishers can only describe the knowledge they possess; they cannot provide a system of knowledge representation that encapsulates all the knowledge that might be required, or deduced, by their customers.
Therefore, great significance and associated business value for provisioning new concepts and concept relationships lies in pushing through these barriers to automate the scaling and proliferation of knowledge representations into brand new application areas. One way to distinguish between existing and new applications is that whereas existing applications might answer, “What knowledge is contained in these documents?”, new applications might answer, “what knowledge can we generate next?” Among the technical barriers to achieving such knowledge creation applications is the provisioning of new mechanisms to define and capture concepts and concept relationships.